Economic and Technical Efficiency of the Biomass Industry in China: A Network Data Envelopment Analysis Model Involving Externalities

Abstract

This paper proposes the network data envelopment analysis (DEA) model accounting for negative externalities and applies it for decomposition of profit inefficiency in the biomass-agriculture circular system (Bio-AG system). A circular structure of the Bio-AG system which is different from the previously applied network structures is assumed. Since the negative externalities (i.e., pollutant emissions from the biomass industry) occur in the Bio-AG system, the property rights are taken into consideration to model the externalities-adjusted profits. Therefore, the changes in profits due to changes in the property rights (assuming no property rights, allocating property rights to agricultural sector, and allocating property rights to biomass power generation sector) are quantified. Further, the decomposition shows that the biomass power generation sector is less affected by technical inefficiency if contrasted to allocative inefficiency in terms of the profit loss. The findings suggest that the biomass power generation technology influences the profits of the biomass industry. What is more, the inefficient allocation of resources is now the key factor undermining performance of the biomass industry. Therefore, the government should adopt measures to improve the allocation of resources and prevent excessive investments or development of less efficient technologies.

Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry

“…In fact, spatial effect is quite common in socio-economic phenomena. Existing research shows that China's NEI has significant spatial disparities [40][41][42]. To further reveal the spatiotemporal heterogeneity influence of driving factors on NEI's technical efficiency, it is appropriate to use a variablecoefficient model to reflect the difference in coefficients across time and space.…”

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

“…First, this review paper found there are various models of DEA have been used in previous studies. The important of DEA models were non-radial DEA (Wang et al [142]; Bian et al [66]), bootstrap DEA (Duan et al [120]), CCR and BCC models (Shi et al [80]; Mousavi-Avval et al [82]; Khoshnevisan et al [83]), DEA window analysis (Vlontzos and Pardalos [102]; He et al [115]), DEA frontier (Jan et al [78]; Lins et al [61]), VRS (Wang and Wei [81]; Zhou et al [97]), DDF (Vlontzos et al [154]; Wang et al [152]), DEA-Malmquist (Martínez and Piña [145]; Huang et al [137]; Wang and Feng [76]), SBM-DEA (Guo et al [108]; Chu et al [109]), DEA-MBP model (Welch and Barnum [72]), network DEA (Wu et al [67]; Yan et al [121]), stochastic DEA (Vaninsky [126]), stochastic network DEA (Chen et al [127]), SFA (Li and Lin [118]; ), radial stochastic DEA (Zha et al [132]), fuzzy dynamic network-DEA (Olfat et al [138]), CRTS and VRTS (Sueyoshi and Yuan [139]), DEA-DA (Chen et al [141]), fuzzy network SBM model (Shermeh et al [147]), Interval DEA-CCR (Gong and Chen [155]) and SE-DEA (Liu et al [159]). In addition, the results found that one previous review study classifies and review the recent DEA models under the methodological aspect, application schemes, efficiency measure, inputs, outputs.…”

An Empirical Analysis on the Relationship Between Green Economy and Inflation in China

“…This is partially attributable to the significant country differences in the status and policies of green economy. On China's green economy, the existing research methods can be divided into four categories: data envelopment analysis (DEA) (Guo et al, 2017;Bao, 2017;Marino et al, 2017;Yan, 2017Yan, , 2018Shen et al, 2017;Han et al, 2018;Song, 2018), the stochastic impacts by regression on population, affluence and technology (STIRPAT) , the logarithmic mean Divisia index (LMDI) model (Wang and Feng, 2018;Chen, 2018), and the GML/ML index. Coupling the DEA and the directional distance function (DDF), the ML index was modified from the Malmquist index (Chung et al, 1997), which is traditionally calculated from the output distance between different technologies.…”